SYSTEMS AND METHODS OF URBAN ROOFTOP AGRICULTURE WITH SMART CITY DATA INTEGRATION

SYSTEMES ET PROCEDES D'AGRICULTURE DE TOIT URBAIN AVEC INTEGRATION DE DONNEES DE VILLE INTELLIGENTE

English Abstract

An IoT-enabled network of urban rooftop farms. Systems and methods include receiving image data; processing the image data with an artificial intelligence model; determining, based on the processing of the image data, a set of pixels of the image data meets one or more thresholds; and based on determining the set of pixels meets the one or more thresholds, recording the set of pixels as a viable agriculture space. Systems and methods also include receiving data from one or more sensors, wherein each of the one or more sensors is associated with an agriculture space; generating one or more metrics indicative of farming viability associated with the agriculture space based on the data received form the one or more sensors; and generating a user interface comprising the generated metrics.

French Abstract

L'Invention concerne un réseau compatible IoT de fermes de toit urbains. Les systèmes et les procédés comprennent la réception de données d'image ; le traitement des données d'image avec un modèle d'intelligence artificielle ; la détermination, sur la base du traitement des données d'image, d'un ensemble de pixels des données d'image satisfaisant un ou plusieurs seuils ; et sur la base de la détermination de l'ensemble de pixels satisfaisant le ou les seuils, l'enregistrement de l'ensemble de pixels en tant qu'espace agricole viable. Des systèmes et des procédés comprennent également la réception de données provenant d'un ou de plusieurs capteurs, chacun du ou des capteurs étant associé à un espace agricole ; la génération d'une ou de plusieurs métriques indiquant la viabilité agricole associée à l'espace agricole sur la base des données reçues en provenance du ou des capteurs ; et la génération d'une interface utilisateur comprenant les métriques générées.

Claims

WO 2022/060940
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CLAIMS
What is claimed is:
1. A method of determining agriculture space viability, the method
comprising:
receiving image data;
processing the image data with an artificial intelligence model;
determining, based on the processing of the image data, a set of pixels of the
image
data meets one or more thresholds associated with agricultural viability; and
based on determining the set of pixels meets the one or more thresholds,
recording
the set of pixels as a viable agriculture space.
2. The method of claim 1, wherein the one or more thresholds comprise one
or
more of a size threshold and an angle threshold.
3. The method of claim 1, further comprising identifying the set of pixels
as a
rooftop.
4. The method of claim 1, wherein the agriculture space is a rooftop.
5. The method of claim 1, wherein the image data is geospatial data
received
from a satellite.
6. The method of claim 1, further comprising, prior to recording the set of
pixels
as the viable agriculture space, estimating a load capacity associated with
the set of pixels.
7. The method of claim 1, further comprising, prior to recording the set of
pixels
as the viable agriculture space, identifying a parapet associated with the set
of pixels.
8. The method of claim 1, wherein recording the set of pixels comprises
updating
an index.
9. The method of claim 1, further comprising, prior to processing the image
data,
receiving a dataset associated with a city.
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10. A user device comprising:
a processor; and
a computer-readable storage medium storing computer-readable instructions
which,
when executed by the processor, cause the processor to execute a method, the
method
comprising:
receiving image data;
processing the image data with an artificial intelligence model,
determining, based on the processing of the image data, a set of pixels of the
image data meets one or more thresholds; and
based on determining the set of pixels meets the one or more thresholds,
recording the set of pixels as a viable agriculture space.
11. The user device of claim 10, wherein the one or more thresholds
comprise one
or more of a size threshold and an angle threshold.
12. The user device of claim 10, wherein the method further comprises
identifying
the set of pixels as a rooftop.
13. The user device of claim 10, wherein the agriculture space is a
rooftop.
14. The user device of claim 10, wherein the image data is geospatial data
received from a satellite.
15. The user device of claim 10, wherein the method further comprises,
prior to
recording the set of pixels as the viable agriculture space, estimating a load
capacity
associated with the set of pixels.
16. The user device of claim 10, wherein the method further comprises,
prior to
recording the set of pixels as the viable agriculture space, identifying a
parapet associated
with the set of pixels.
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17. The user device of claim 10, wherein recording the set of pixels
comprises
updating an index.
18. The user device of claim 10, wherein the method further comprises,
prior to
processing the image data, receiving a dataset associated with a city.
19. A computer program product comprising:
a non-transitory computer-readable storage medium having computer-readable
program code embodied therewith, the computer-readable program code
configured, when
executed by a processor, to execute a method, the method comprising:
receiving image data;
processing the image data with an artificial intelligence model;
determining, based on the processing of the image data, a set of pixels of the
image
data meets a size threshold and an angle threshold; and
based on determining the set of pixels meets the size threshold and angle
threshold,
recording the set of pixels as a viable agriculture space.
20. The computer program product of claim 19, wherein the one or more
thresholds comprise one or more of a size threshold and an angle threshold
21. A method of monitoring agriculture space, the method comprising:
receiving data from one or more sensors, wherein each of the one or more
sensors is
associated with an agriculture space;
generating one or more metrics indicative of farming viability associated with
the
agriculture space based on the data received form the one or more sensors; and
generating a user interface comprising the generated metrics.
??. The method of claim 21, wherein the one or more metrics
comprise one or
more of a temperature, a precipitation level, wind event data, air pressure
change data,
fertilization indicator, and a UHI temperature index.
23 The method of claim 21, further comprising generating one
or more
recommendations based on the data received from the one or more sensors,
wherein the one
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or more recommendations comprise one or more of a water recommendation,
fertilizer
recommendation, crop recommendation, planting recommendation, harvesting
recommendation, and soil augmentation recommendation.
24. The method of claim 21, wherein the data comprises data processed by a
processor of a device comprising the sensor.
25. The method of claim 21, further comprising processing the data received
from
the one or more sensors.
26. The method of claim 21, further comprising determining a location of
the one
or more sensors.
27. The method of claim 21, wherein the one or more sensors comprise one or
more of a soil sensor and a weather sensor.
28. The method of claim 21, wherein the data is received from the one or
more
sensors via a field gateway.
29. The method of claim 21, wherein the user interface comprises a summary
of
data associated with the sensors.
30. The method of claim 21, further comprising generating one or more
recommendations based on the data received from the one or more sensors,
wherein the user
interface further comprises the recommendations.
31. A user device comprising:
a processor; and
a computer-readable storage medium storing computer-readable instructions
which,
when executed by the processor, cause the processor to execute a method, the
method
comprising:
receiving data from one or more sensors, wherein each of the one or more
sensors is associated with an agriculture space;
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generating one or more metrics indicative of farming viability associated with
the agriculture space based on the data received form the one or more sensors;
and
generating a user interface comprising the generated metrics.
32. The user device of claim 31, wherein the one or more
metrics comprise one or
more of a temperature, a precipitation level, wind event data, air pressure
change data,
fertilization indicator, and a UHI temperature index.
33 The user device of claim 31, wherein the method further
comprises generating
one or more recommendations based on the data received from the one or more
sensors,
wherein the one or more recommendations comprise one or more of a water
recommendation,
fertilizer recommendation, crop recommendation, planting recommendation,
harvesting
recommendation, and soil augmentation recommendation.
34. The user device of claim 31, wherein the data comprises data processed
by a
processor of a device comprising the sensor.
35. The user device of claim 31, wherein the method further comprises
processing
the data received from the one or more sensors
36. The user device of claim 31, wherein the method further comprises
determining a location of the one or more sensors.
37. The user device of claim 31, wherein the one or more sensors comprise
one or
more of a soil sensor and a weather sensor.
38. The user device of claim 31, wherein the data is received from the one
or more
sensors via a field gateway.
39. The user device of claim 31, wherein the user interface comprises a
summary
of data associated with the sensors.
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40. A computer program product comprising:
a non-transitory computer-readable storage medium having computer-readable
program code
embodied therewith, the computer-readable program code configured, when
executed by a
processor, to execute a method, the method comprising:
receiving data from one or more sensors, wherein each of the one or more
sensors is
associated with an agriculture space;
generating one or more metrics indicative of farming viability associated with
the
agriculture space based on the data received form the one or more sensors, and
generating a user interface comprising the generated metrics.
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Description

WO 2022/060940
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SYSTEMS AND METHODS OF URBAN ROOFTOP AGRICULTURE WITH
SMART CITY DATA INTEGRATION
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to US Provisional
Application No. 63/080,748,
filed on September 20, 2020, the entire contents of which are hereby
incorporated by
reference.
TECHNICAL FIELD
[0002] The present invention relates to urban remote sensing,
geographical information
systems (GIS), data analytics, Internet of Things (loT) cloud-connected
sensors, and smart
city data networks, and more particularly, though not exclusively, to smart
cities, food
security, and climate mitigation programs using satellite based geospatial
data combined with
custom GIS, artificial intelligence (Al) analytics, and IoT sensors to index,
monitor and
connect rooftop urban agriculture networks.
BACKGROUND
[0003] As the world develops and the population of the world
increases, the need for
agriculture creates a requirement for GIS to be utilized in cities for mapping
and
infrastructure planning. Contemporary means and uses of GIS involve
determining solar
viability for rooftops but fail to provide accurate or useful tools for
determining whether
urban spaces, such as rooftops, may be used for agriculture. For example,
current systems
lack effective quantitative ranking criteria and protocols for agriculture
development of urban
rooftops. An automated methodology is needed to develop a comprehensive
dataset which is
scalable and effective in cities across the globe.
[0004] While crop monitoring via sensors may be a viable option
in rural areas, urban
interfaces cause a host of issues surrounding latency and situational
awareness regarding
position, velocity, and time (PVT) and loss of sight (LOS). Furthermore,
network
connectivity can be unreliable in urban areas, due to building materials and
geometries,
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leading to delayed data sync schedules, which interferes with near real-time
transfer and
analysis of both ground-based and space or air-based information.
100051 Monitoring rooftop agriculture on a city-wide scale
requires very high resolution
(VHR) imagery due to intercropping, i.e., growing two or more crops within
close proximity.
Since each plant has its own spectral signature, the intermingling of crops
can result in
biological interaction, making health determinations difficult to accurately
track and measure
Therefore, VHR imagery is highly useful in providing <1 meter spatial
resolutions for precise
data capture and analytics training and calculations with Normalized
Difference Vegetation
Index (NDVI) or Solar Induced Fluorescence (SIF) algorithms. VHR, medium
resolution,
and low resolution datasets may all be utilized to ensure continuity in data
capture and
assessment for monitoring crop growth, health, and yield forecasting.
100061 Finally, contemporary cloud-based platforms cause concerns
related to latency,
distribution, and scalability, particularly as the number of IoT devices
connected to these
networks continues to rise and associated data bottlenecks form.
SUMMARY
100071 In one exemplary embodiment of the present technology, at
least the
aforementioned disadvantages can be mitigated or overcome by integrating a
networked
urban agriculture IoT sensor platform into a smart city data platform,
encompassing three
central components: (1) identifying and indexing viable infrastructure
(through geospatial
data, machine learning, and GIS), (2) monitoring and managing rooftop urban
agricultural
farms (through geospatial data, IoT sensors, and At analytics), and (3)
connecting both crop
data and tertiary weather and climate metrics to a smart city data network
(through IoT
sensors and cloud or cloudlet-enabled data networks).
100081 One exemplary advantage of the systems and methods
described herein enables
consumers to make more useful and measurable choices regarding urban food
security and
sustainability projects. Another exemplary advantage of the systems and
methods described
herein enables cities to extract quantitative environmental data to measure
progress or
compare against projected outcomes of green infrastructure developments.
100091 According to one or more of the embodiments described
herein, a process for
remotely monitoring an urban agriculture system may include using very high
resolution
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(VHR) optical, multispectral, and/or synthetic aperture radar (SAR) satellite
data and/or other
forms of aerial data for remotely sensed imagery. One or more IoT sensors may
be used for
remotely sensing data from crops, soil beds, weather, or any other related
environmental
source. One or more microprocessors, or other processing devices, may be used
for AT
analysis of collected sensor data. A normalized difference vegetation index
(NDVI)
algorithm may be implemented to monitor crop health. Solar-induced
fluorescence (SFI)
algorithms may be used to monitor crop stress. Field gateways may facilitate
data collection
and compression and filtering before the data is moved to the cloud. Cloud or
cloudlet data
centers may collect, analyze, and distribute data from sensors and/or aerial
and/or satellite
sources, while decentralizing data storage and compute processes. A data lake
or lakehouse
may be configured to store captured IoT sensor data. One or more extract,
transform, and
load (ETL) or extract, load, and transform (ELT) systems may be configured to
extract,
transform, and load the data into storage or warehouse with structure for
future querying of
historical data. One more APIs may be configured to enable users to collect
and utilize
agricultural data (e.g., both current and historical) across a smart city data
network. An
application-based software platform (mobile or web-hosted) for distributing
analyzed and
harmonized data may be implemented and a software platform allowing for the
manipulation
of collated data points and visualized in graphical model or statistical
format to suit various
application or planning needs may be provided
100101 According to one or more of the embodiments described
herein, a smart city
data network infrastructure may include the use of very high resolution
optical, multispectral,
SAR satellite, and/or aerial data. GIS may be used with layers indicating all
rooftop
agriculture in operation and all viable rooftops may be identified and
earmarked for future
development. Field gateways may facilitate data collection, compression, and
filtering before
the data is moved to the cloud. Cloud or cloudlet data centers may be
configured to collect,
analyze, and disseminate data from aerial/satellite imagery, while also
decentralizing data
storage and data processing. A data platform may securely capture, collate,
and analyze a
distributed network of agricultural and weather related data from farms
throughout the city.
Secure and accessible data and datasets may be segmented for various city
departments based
on needs and use cases. Both individual farm data and city-wide collective
data insights
developed through machine learning (ML) analytics may ensure city departments
have
visibility and predictability regarding crop yields or climate metrics and can
plan policy or
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outreach accordingly. ML algorithms and neural networks may be implemented to
transform
cumulative data into patterns and predictions regarding city-wide crop health,
yield forecasts,
microclimate weather forecasts, and farm operations. One or more APIs may be
developed
for ease of collecting and distributing agricultural and microclimate data
(both current and
historical) across a smart city data network. Application-based software
platform (mobile or
web-hosted) may be used for distributing analyzed and harmonized data. A
software
platform allowing for the manipulation of collated data points and
visualization of data in
graphical models, or a statistical format may be used to suit various
application or planning
needs.
100111
According to one or more of the embodiments described herein, a city-
specific
rooftop indexing and ranking system for agriculture implementation may be
implemented.
Such a system may comprise the use of very high resolution optical,
multispectral, SAR
satellite, and/or aerial data for remotely sensed imagery. LiDAR may be used
for
determining building height, volume, and useable area for rooftops. ML
algorithms and
neural networks may optimize and identify rooftop infrastructure based on
multiple criteria
decision analysis (MCDA). A digital roadmap including a custom GIS may index
all
available and viable rooftops in a given operational area, neighborhood, or
city to enact a
network of rooftop agriculture. Index parameters for a developed algorithm may
include
contiguous area, pitch, slope, building height, etc. A digital roadmap may
also include a
custom GIS ranking all available and viable rooftops in a given operational
area,
neighborhood, or city to enact a network of rooftop agriculture Rankings may
be displayed
in a user interface according to minimum useable area calculations, or another
subset of
criteria related to city-specific data. One or more APIs may be developed for
collecting and
distributing data (both current and historical) across a smart city data
network. An
application-based software platform (mobile or web-hosted) may be used for
distributing
analyzed and harmonized data. An application-based software platform (mobile
or web-
hosted) for manipulating data or for toggling on and off layers of interest
and for displaying
information relating to desired criteria and overlapping of information.
[0012]
According to one or more of the embodiments described herein, a city-
specific
roadmap for implementing three levels of rooftop sustainability measures may
include a
digital roadmap that includes a custom GIS classifying all available and
viable rooftops in a
given operational area, neighborhood, city. Layers of the GIS may include
ratings indicating
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best options to implement one of three levels of sustainability projects. For
example, to
address albedo, GIS may be used to identify all dark rooftops that can be
painted white for
added energy savings through reflectivity. To address solar applications, GIS
may be used to
identify all viable rooftops for solar investment and deployment. To address
agriculture
application, GIS may be used to identify all viable rooftops, at or above
outlined thresholds,
for agriculture implementation. One or more layers may incorporate city-
specific parameters
like socio-economic factors, UHI, and food deserts (distance from rooftop to
geographic
point). One or more layers may identify and quantify existing urban rooftop
green spaces or
solar installations. One or more MIL algorithms may utilize neural networks to
index and
create a ranking system based on an analysis of geospatial data, building
information, and
socio-economic inputs specific to a particular city. An application-based
software platform
(mobile or web-hosted) may be implemented for distributing analyzed and
harmonized data.
The application-based software platform may be utilized by a user for
manipulating data or
for toggling on and off layers of interest and to display desired criteria and
view overlaps of
information. The application-based software may also enable report generation
with graphic
and statistical display of quantitative information to support policy and
decision making.
100131 According to one or more of the embodiments described
herein, a city-wide
STEM education platform may include applications of school green roofs with
areas
designated for agriculture implementation including, for example, a technology
platform
comprising both hardware and software for STEM engagement. Such an embodiment
may
comprise the use of very high resolution optical, multispectral, SAR
satellite, and/or aerial
data for remotely sensed imagery. One or more IoT sensors, for remotely
sensing data from
crops, soil beds, and/or weather, or other any other related environmental
source, may be
deployed. Microprocessors, or other processing devices, may be used for AT
analysis of
collected sensor data. A normalized difference vegetation index (NDVI)
algorithm may be
configured and used to monitor crop health. A solar induced fluorescence (SIF)
algorithm
may be used to monitor crop stress. One or more field gateways may be
implemented to
facilitate data collection, compression, and filtering before the data is
moved to the cloud.
Cloud or cloudlet data centers may be used to collect, analyze, and distribute
data originating
from sensors and/or aerial/satellite sources, and to decentralize data storage
and
computational processes. A data lake or lakehouse may be used for storing
captured IoT
sensor data. ETL or ELT may be used for extracting, transforming, and loading
the data into
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storage or a warehouse with structure for future querying of historical data.
One or more
APIs may be developed for ease of collecting and utilizing agricultural data
(both current and
historical) across a smart city data network. An application-based software
platform (mobile
or web-hosted) may be used to distribute analyzed and harmonized data. A
school-targeted
mobile or app-based software platform allowing for the manipulation of
collated data points
and visualization of data in a graphical model or statistical format to suit
various application
or planning needs may be implemented. A visibility across platform designated
specifically
for schools may enable users to monitor individual rooftop farm performance
and metrics, as
well as city-wide performance. A platform may display information related to
resource
utilization in school rooftop crop management and growing cycles. A platform
may also
display quantitative growth stages, yield expectations, and once harvested,
alerts may be used
to show direct benefit to the school lunch programs. Such an embodiment may
create
opportunities for visibility between a number of participating schools and
promote healthy
competitions for novel data usage and analysis, spin-off business
opportunities, and
community engagement.
100141 According to one or more of the embodiments described
herein, a city-wide
green infrastructure or green project monitoring program may include the use
of very high
resolution optical, multispectral, SAR satellite and/or aerial data for
remotely sensing
imagery. One or more IoT sensors may be used for remotely sensing data from
crops, green
roofs, soil beds, green parklets, and/or weather or other any other related
environmental
source One or more microprocessors or other processing devices may be used for
AT
analysis of collected sensor data. A normalized difference vegetation index
(NDVI)
algorithm may be used to monitor crop health, evapotranspiration (ET), and
crop
evapotranspiration (ETc) to quantify a rate of plant transpiration. Such data
may be used to
estimate or determine a cooling effect of green infrastructure projects.
Sensors may monitor
temperature of both air (urban heat island (UHI)) and soil, providing an
indication of changes
between the two data points, such as on an hourly or daily basis. Such data
may provide an
insight into irrigation timing and quantity, leading to more sustainable use
of critical
resources. One or more field gateways may be used to facilitate data
collection, compression,
and filtering before the data is moved to the cloud. Cloud or cloudlet data
centers may be
used to collect, analyze, and distribute data from sensors and/or
aerial/satellite sources, while
decentralizing data storage and computational processes. A data lake or
lakehouse may be
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used for storing captured IoT sensor data. ETL or ELT may be used for
extracting,
transforming, and loading the data into storage or a warehouse with structure
for future
querying of historical data. One or more APIs may be implemented to ease of
collecting and
utilizing agricultural data (both current and historical) across a smart city
data network. An
application-based software platform (mobile or web-hosted) may be used for
distributing
analyzed and harmonized data. A software platform may allow for the
manipulation of
collated data points and the visualization of data in a graphical model or
statistical format to
suit various application or planning needs. The software platform may also be
configured to
generate reports with graphic and statistical display of quantitative
information to support
policy and decision making.
[0015] According to one or more of the embodiments described
herein, a scalable
program for identifying urban rooftops, developing agriculture, and/or fields,
and for
monitoring and measuring results of an urban carbon capture program. Such an
embodiment
may include, for example, the use of very high resolution optical,
multispectral, SAR
satellite, and/or aerial data for remotely sensing imagery. One or more IoT
sensors may be
used for remotely sensing data from crops, soil beds, weather and/or other any
other related
environmental source. One or more microprocessors or other processing devices
may be
implemented for AT analysis of collected sensor data. A normalized difference
vegetation
index (NDVI) algorithm may be configured to monitor crop health. A solar
induced
fluorescence (SIF) algorithm may be configured to monitor crop stress. A long
term urban
soil CO2 capture plan may be implemented based on a network of rooftop
agriculture sites
using rotational cover crop and non-tilled field methods. As used herein, an
agriculture site
may be a rooftop area capable of being used as a farm or garden. The term
agriculture site
may be interchangeable with agriculture space, agriculture site, farm, garden,
etc. A network
of sites may be measured both remotely via annual crop yields and locally
through soil
sampling via, for example, a third party. One or more field gateways may be
used to
facilitate data collection, compression, and filtering before the data is
moved to the cloud.
Cloud or cloudlet data centers may be used to collect, analyze, and distribute
data from
sensors and/or aerial/satellite sources, while decentralizing data storage and
computational
processes. A data lake or lakehouse may be used for storing captured IoT
sensor data. ETL
or ELT may be used for extracting, transforming, and loading data into storage
or a
warehouse with structure for future querying of historical data. One or more
APIs may be
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developed for ease of collecting and utilizing agricultural data (both current
and historical)
across a smart city data network. An application-based software platform
(mobile or web-
hosted) may be developed for distributing analyzed and harmonized data. A
software
platform may allow for the manipulation of collated data points and the
visualization of data
in a graphical model or statistical format to suit various application or
planning needs. The
software platform may also be configured to generate reports which display
quantitative
carbon capture results of both individual rooftop sites and/or an entire urban
agriculture
network throughout a given area, community, city, or region.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Fig. 1 is an illustration of a smart city urban
agriculture data management
system 100 according to one or more embodiments of the present disclosure;
[0017] Fig. 2 is an illustration of an information flow diagram
200 for a smart city
urban agriculture data network according to one or more embodiments of the
present
disclosure;
100181 Fig. 3 is a flow chart of a method for developing the
rooftop index and ranking
system algorithms according to one or more embodiments of the present
disclosure;
[0019] Fig. 4 is a block diagram of a data processing system or
agriculture network data
platform for smart city indexing, monitoring, and connecting rooftop data and
transforming it
into usable data for a multitude of user profiles according to one or more
embodiments of the
present disclosure; and
[0020] Figs. 5-8 are flowcharts of methods according to one or
more embodiments of
the present disclosure.
[0021] Preferred features, embodiments, and variations of the
technology may be
discerned from the following Detailed Description which provides sufficient
information for
those skilled in the art to perform the technology. The Detailed Description
is not to be
regarded as limiting the scope of the preceding Summary of the technology in
any way.
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DETAILED DESCRIPTION
100221 As described above, mapping for viable rooftops to
implement urban rooftop
agriculture at scale is needed. An automated methodology is needed to develop
a
comprehensive dataset, scalable and effective in cities across the globe.
Further, current
models lack effective quantitative ranking criteria and protocols for
agriculture development
of urban rooftops.
100231 Supervised classification for urban agriculture
designation is not currently
baselined. This is essential for determining existing green roof and
agriculture rooftop sites
and outlining future development goals for urban sustainability projects in
conjunction with
city specific economic, social, and environmental factors. Current rooftop
evaluation models
do not utilize the same benchmarks and standards for classification globally,
as regional
approaches and anticipated application outcomes vary.
100241 Crop monitoring via sensors is a viable option in rural
areas. Urban interfaces
cause a host of issues surrounding latency and situational awareness regarding
position,
velocity, and time (PVT) and loss of sight (LOS). As well, network
connectivity can be
unreliable in urban areas, due to building materials and geometries, leading
to delayed data
sync schedules, which interferes with the near real-time transfer and analysis
of both ground-
based and space or air-based information.
100251 Remote monitoring of urban infrastructure via satellite or
aerial can be
performed utilizing low to medium resolutions. Monitoring rooftop agriculture
on a city-
wide scale requires very high resolution (VHR) imagery due to intercropping
(growing two
or more crops within close proximity). Since each plant has its own spectral
signature, the
intermingling of crops can result in biological interaction, making health
determinations
difficult to accurately track and measure. Therefore, VHR imagery is highly
useful in
providing <1 meter spatial resolutions for precise data capture and analytics
training and
calculations with Noimalized Difference Vegetation Index (NDVI) or Solar
Induced
Fluorescence (S1F) algorithms. Vi-1R, medium resolution, and low resolution
datasets may
all be utilized to ensure continuity in data capture and assessment for
monitoring crop
growth, health, and yield forecasting.
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[0026] Remote monitoring of non-contiguous fields /rooftops in an
urban environment
has not been attempted at a city-wide scale. Precision agriculture monitoring
leverages
geospatial data as a valuable tool for getting a complete picture of
distributed fields. Those
fields can be tens to hundreds, or even thousands of acres, which translate
into time and
money for a set of eyes to continually monitor all corners of the operations.
Geospatial data
with a temporal cadence of 12-24 hours provides complete and timely
information about
every field every day. The same application of coverage is not performed in
urban areas,
since city-wide rooftop farms at scale do not currently exist. However,
despite the
environmental difference, the elevation, and the more extreme example of non-
contiguous
fields, the monitoring aspects remain the same. Hence, the fields spread out
over multiple
rooftops comprise soil, crops, and weather at its core. Utilizing geospatial
or UAV or aerial
imagery can be leveraged to develop better, more sustainable, and more
efficient agricultural
and crop production outcomes for urban agriculture.
[0027] Some cloud-based platforms cause concerns related to
latency, distribution, and
scalability, particularly as the number of IoT devices connected to these
networks continues
to rise and the associated data bottlenecks form. The introduction of 5G and
edge computing
capabilities currently enable modest compute and storage at the sensor level,
opening new
opportunities to reduce latency and increase security. While some difficulty
may be
encountered regarding handling the full bandwidth of Ag-IoT data ingestion and
ETL
requirements locally, there are some interesting avenues the urban environment
opens up
regarding processing at the device level to reduce latency and enable near
real-time compute
and analysis of crop and weather data.
[0028] As illustrated in Fig. 1, an exemplary smart city urban
agriculture data
management system 100 according to one or more embodiments of the present
disclosure
may comprise a network 115 in communication with one or more computing
elements. A
smart city foundations may be based on an information and communications
technology
(ICT) framework. One exemplary aspect of the present technology relates to an
IoT-enabled
network of urban agriculture for smart city integration comprising three main
components
built on an ICT framework.
[0029] Indexing existing viable infrastructure for urban
agriculture development may
be performed using one or more satellites 105 to acquire multispectral data.
Multispectral
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data may be used to create a viable infrastructure index for GIS 110.
Satellite and aerial
geospatial data packages may in some embodiments be provided by a third party
vendor
and/or other sources. The GIS platform may be maintained through a third party
host or
another provider. Data acquired by satellites 105 may be transmitted and
processed through
either a cloud or cloudlet network 115 for greater efficiency, though it
should be appreciated
other transmission and/or processing options may be used, such as by using one
or more
localized distributed networks. Data may move between the GIS 110 and the
cloud 115 as
new information and changes in remotely sensed data is collected in, for
example, a 24-48
hour cadence, although any other collection cycle is possible.
100301 Updates may be reflected in application-based platforms on
user devices such as
a smartphone or tablet 130 and computing devices 135.
100311 Crop and soil sensors 125a, 125b and weather sensors 125c
may be configured
to collect data at a roof level. Data collected by crop and soil sensors 125a,
125b and weather
sensors 125c may be processed either locally using on-board microprocessor and
AT
algorithms or after being transmitted through a field gateway 120 to the
network 115 for
interpretation and distribution to app-based devices. Results based on the
processed data may
be distributed to individuals within the network and/or to groups or managers
of the network,
with the data optionally being summarized, localized, geographically filtered
and/or any other
known method of presenting data appropriate for use by the user.
100321 According to certain embodiments, sensors 125a, 125b, 125c
may be plug and
play compatible with the field gateway 120. The sensors 125a, 125b, and 125c
may be able
to transmit data with little configuration. As such, changing sensors and
coupling with the
field gateway 120 may be a straightforward process enabling changing sensors
with little
down time.
100331 A smart city urban agriculture data management system 100
may be used by, for
example, urban farmers reading daily crop metrics and leveraging predictive
farming to
reduce risks and increase efficiency and city governments leveraging collated
and quantified
metrics regarding food production and urban heat islands (UHI) at a number of
sites and at
city-scale. An app-based platform executing on user devices 130, 135 may be
used to
provide actionable and amplified metrics to be used, for example, for
governing insights.
Metrics generated through the systems and methods described herein may be
metrics
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indicative of farming viability. For example, soil or air temperatures,
precipitation levels,
wind event data, air pressure change data, fertilization indicators, UHI
temperature indices,
etc. may each be a type of metric generated using sensor data as described
herein which may
be indicative of whether a particular space may be viable for farming
purposes.
100341 In some embodiments, a method involving a system 100 as
described herein
may begin with a satellite 105 which may be operated by a third party
collecting geospatial
data which may be input to a third party GIS platform 110. Visual and SAR data
collected by
the satellite 105 may be analyzed with an overlay of NDVI algorithms to
classify all existing
urban agriculture sites within designated city limits or other geographic
boundaries.
100351 A following layer of city data sets may be added to the
GIS and may include
metrics for analysis by an algorithm configured to identify viability of
existing rooftops of a
city for agriculture implementation. With the newly created layers of urban
agriculture and
viable rooftops, one or more entities such as a city planning commission or
other city
development departments may identify and establish a plan for implementing an
urban
agriculture network of rooftop farms. Ground truthing and engineering checks
may be used
to verify the validity of the remote and geospatial identifications.
100361 Once agricultural sites are developed, one or more IoT-
enabled soil and/or
weather sensors 125a-c may be installed in or around soil beds at each site.
For example, a
rooftop garden may comprise one or more soil beds and each soil bed may
include a separate
soil sensor and a weather sensor may also be placed on the rooftop. One or
more satellites
105 may be used to monitor and capture geospatial data relating to sites
within a geographical
region on a periodic rotation. Such data may be transmitted to a cloud-based
network 115.
Collected and sensed data may be transmitted from sensors 125a-c through a
field gateway
120 to the network 115 and/or one or more cloud-based or cloudlet hubs. Next,
the
geospatial data may be analyzed with, for example, AT and/or NDVI algorithms
to assess
daily crop health. The field gateway 120 can operate as a collection point for
the data and
may pre-process and filter the data before transmitting the data to the cloud
network 115.
100371 The data may be updated in real- or near real-time or in
intervals such as hourly,
daily, or weekly, and may be accessed using on, for example, an app-based
platform using a
user device 130, 135, where a user such as a farmer will have access to the
metrics outlined
above. With those insights and analyzed data, resource application and
management can be
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tailored to meet changing environmental conditions. In some embodiments, site-
specific
metrics may be provided to a farmer and each site's metrics may be distributed
to a city's
smart city data platform which may be accessed using a user device 130, 135,
which may be
an extension of an app-based platform for farmers.
100381 Based on the data, recommendations may be provided to a
farmer, such as water
recommendations, fertilizer recommendations, crop recommendations, planting
recommendations, harvesting recommendations, soil augmentation
recommendations,
collaborative farming recommendations, etc. Benchmarking data points like
daily
precipitation along with hourly temperature and humidity, leaf wetness, soil
temperature, and
evapotranspiration ETc can inform timing and duration of spraying -
irrigation. A graphic
user interface (UI) allows the farmer to plot each sensor's hourly data sync
and derive
actionable insight based on the near real-time information. If paired with a
third party smart
irrigation system, a series of parameters or trigger points can be set to
initiate spraying
automatically with targeted amounts in areas that fall below specified
targets. Similarly,
tracking growing degree days (GDD) or heat units, offers a way to estimate
growth and
development of both crops and pests. With the accumulation of average daily
temperatures,
there is a minimum development threshold that is required for growth. GDD is a
more
accurate way to anticipate crop development and predict yield, as well as
anticipate timing of
pest development. Therefore, the timing of any pesticide spraying can be
better predicted and
managed using the sensor's cumulative GDD data High resolution NDVI imagery
helps to
alert farmers to potential issues, as higher numbers indicate a better
greenness score Lower
numbers may indicate a stressed crop, due to either pest infestation or
drought. This can alert
farmers to spray fertilizers in the monitored areas or potential irrigation
issues. The benefits
of these actionable insights, means lower resource utilization and better
efficiencies in
growing. Finally, incorporating yield forecasting from all farms across the
network will
provide better planning for city agencies to allocate food appropriately. A
decentralized PaaS
will collate, analyze, and display daily updates across the network.
100391 In some embodiments, a user may be a representative of a
city or other form of
municipality or entity and such a user may be described as a "city user." The
city user may
be enabled to access data collected from a number of urban agriculture sites.
For example, a
city user may have access to data associated with all urban agriculture sites
within a
geographical area such as within the city limits. This data can be analyzed
using machine
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learning configured to uncover trends and patterns in food production,
temperature changes
around the sites, cumulative and temporal changes to stormwater capture, and
other
microclimate metrics. Analysis data may be made available on a user's app
platform and can
be merged with other smart city data projects on a cloud management platform.
The app
platform may be a third party product.
100401 The app-based platform may be a Platform as a Service
(PAAS) or Software as
a Service (SAAS) providing computer services for creating multi-level and
multi-level access
permissions for a community of users to participate in discussions, share
insights into
metrics, engage in virtual business or governmental hearings, such as where
rights are
granted. The PAAS may comprise a software platform for providing an on-line
portal to
review and manipulate data in predetermined categories or views, share and
control data
access with designated employees, engage in operational logistics, or conduct
virtual multi-
department meetings.
100411 As illustrated in Fig. 2, a smart city urban agriculture
data network may operate
according to an information flow process 200. The information flow process 200
illustrates a
cycle of data capture from an initial stage to a complete smart city data
network platform and
user application.
100421 In some embodiments, a network may be associated with a
group or set of
agriculture units. An agriculture unit may be, for example, a rooftop garden.
Each
agriculture unit may be associated with one or more sensors. A network may
require a
minimum number of agriculture units and/or a minimum number of sensors. For
example, in
a particular exemplary embodiment, a minimum of three rooftops with a minimum
of three
sensors (one on each rooftop) qualifies as a network. The benefits of the
smart city data
network rest on the quality of the captured data, the analysis of the collated
data, and the
actionable insights derived and disseminated by the data. In short, a complete
data platform
allows a user to make informed decisions based on the insight derived from the
three data
components (index, monitor, connect).
100431 The information flow process 200 may begin with a series
of steps involving
indexing data. At 10, geospatial data may be captured from one or more
satellites or aerial
devices. For example, single or multiple data points may be captured from
aerial devices
such as planes or drones or satellites. Optical data captured by aerial
devices or satellites
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may be of a medium resolution between three to five meters. The geospatial
data may next
be transmitted to a cloud-based network and may provide a base layer for a
customer's GIS
and/or a roadmap for implementing an urban rooftop agriculture network.
[0044] At12, local and city specific datasets such as zoning and
cadastral information
may be obtained from one or more data storage locations at 12 The local and
city specific
datasets may be combined with the geospatial data of 10 to enable a user such
as a customer
to cross-reference optical data from the geospatial data of 10 with building-
specific details
such as land or property ownership (public/private), building height, roof
slope (e.g., less than
1:12), year built, and construction type. Using LiDAR data and building
footprints, a series
of algorithms may be developed to extract key features for the Index segment.
Multiple-
criteria decision analysis (MCDA) may be employed to develop weighted layers,
and to allow
the index to rank all viable rooftops, in a designated radius, for the
specific function desired
(e.g., addressing food security or climate resilience).
[0045] At 14, a classification system may be configured to
identify and index all viable
rooftops for urban agriculture development within the area represented by the
geospatial data
based on the combined geospatial data in the local and city-specific datasets.
the identified
viable rooftops may be integrated in a new GIS layer.
[0046] At 16, a layer of geospatial data analysis utilizing very
high resolution (VHR)
optical imagery (e.g., less than meter) may be incorporated. The VE1R optical
imagery may
enable the employment of supervised classification techniques within the GIS
system and
may be used as a basis for a new standardized classification for urban
agriculture. This
dataset is an important marker for establishing existing food production
pathways and
capabilities in the customer's geographic area.
[0047] With the previous elements, a planned urban agriculture
network
implementation strategy can be designed and deployed at 18. Using the newly
developed
GIS layers for viable rooftop index and existing urban agriculture as a guide,
sites for
agriculture development can be identified and prioritized based on spatial
information and
relationships.
[0048] The second component of the urban agriculture smart city
data network is
monitoring the indexed data. Once urban rooftop farms are operational, each
may require
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constant management and monitoring via remote and ground-based approaches.
Additionally, captured, and processed data may be transmitted back to a
decentralized smart
city network at intervals or in real-time or near real-time for further study
and evaluation of
the combined metrics.
100491 In some embodiments, scalar viewing may be implemented
using very high
resolution (VHR) optical, multispectral, hyperspectral, and/or synthetic
aperture radar (SAR)
resolutions. At 20, sub-meter optical resolutions, multispectral resolutions
in the near
infrared (NIR) wavelengths, and SAR may be incorporated for monitoring crop
health.
[0050] Optical resolutions may be used to provide a visual field,
multi spectral (NIR)
may be used to provide a basis of crop health algorithms (NDVI), hyperspectral
may be used
to provide a basis for SFI, and SAR may be used to provide around the clock,
rain-or-shine
radar images which are also valuable for crop health monitoring. Geospatial
data and IoT
sensor data may be extracted using ETL/ELT or similar data architecture to
store or
warehouse in, for example, a data lake or lakehouse for future querying and
analysis.
100511 On the ground, one or more sensors may be used to collect
data. In some
embodiments, each agricultural space, such as a rooftop, may comprise a
minimum of one
sensor or a group of sensors. The sensors may be configured to take readings
of various crop
data points on an interval, such as hourly or daily, or in real time. At 22,
the data points from
the sensors may include some or all of the following: soil moisture, soil pH
levels, leaf
health, growth measurements, evapotranspiration, temperature, humidity, wind
speed, wind
direction, solar radiation / UV, rain collection levels, air quality (micro-
particle counts), etc.
100521 Sensor data may be processed in a variety of ways. In a
first scenario, data may
be processed at 24 via a processing device such as a microprocessor on a IoT
sensor device
comprising the sensor. For example, an IoT sensor device may comprise a sensor
such as a
soil sensor. The IoT sensor device may further comprise a processor, memory,
input/output,
and other computing device components. By including a processor within the IoT
sensor/device, latency, energy usage, bandwidth issues, and security concerns
relating to
long-range data transmission may be reduced. In such an embodiment, algorithms
can be
loaded directly onto the IoT sensor device. For example, a processor on an IoT
sensor device
may be configured to process sensor data according to an algorithm for crop
health
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monitoring. The algorithm for crop health monitoring may include, for example,
a
normalized difference vegetation index (NDVI):
=
100531 NDVI NIR - RED
NIR + RED=
100541 Using a sensor as part of an IoT sensor device may be
particularly applicable to
urban areas in which networks such as 5G networks are implemented and which
may enable
edge computing to be a valid option for data capture and analysis.
100551 In some embodiments, instead of or in addition to
processing sensor data using a
processor of an IoT sensor device, data may be transmitted from a sensor to a
field gateway
which may broadcast data to a cloud or cloudlet at 26 where the data may be
collated,
analyzed, and disseminated via an application based software. Such an
embodiment may be
more widely applicable to cities with variable levels of data bandwidth.
100561 IoT sensors may be configured to transmit data to one or
more satellites where
possible and/or to a field gateway before the cloud network. In some
embodiments, the IoT
sensors may be configured to transmit data at, for example, 700 to 2600 MHz on
the NB-IoT
network and/or 868 to 915 MHz on the LoRa network. Processed data may be
disseminated
to one or more users through a digital application-based platform at 28. The
digital platform
may be configured to provide forecast metrics, such as hourly, daily, and/or
weekly metrics,
and also site-specific and/or collated total platform metrics. Such metrics
may include, for
example, a list of each monitored site showing location data, current or past
temperature and
precipitation levels; a monthly outlook with daily temperature and rainfall
predictions for
each site; a map view with fixed location pins for each monitored site;
weather alerts for
temperature spikes, wind event data, air pressure change data; urban field
and/or rooftop soil
mapping, hydrology mapping, and crop modelling; fertilization indicators; a
UHT temperature
index; user input for ground-truthing remote monitoring data; a cumulative
time-based
analysis of location specific metrics across all inputs; etc.; and/or some
combination thereof
100571 In some embodiments, the collected aerial/satellite data
may be merged and
analyzed with IoT sensor data and used to generate a reduced risk asset
management tool for
urban farmers and/or an application based circular data platform for a user to
measure smart
city project metrics.
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100581 The third component of the urban agriculture smart city
data network is
connected. An IoT sensor network may be achieved at 30 through reaching a
minimum
viable number of agriculture locations such as rooftops and/or a viable number
of sensors. In
a particular embodiment, the minimum number of agriculture locations and the
minimum
number of sensors is three, but the maximum is unlimited, in such an
embodiment, three
sensors on three separate rooftops forms the minimum network for feedback and
data
parameters.
100591 At 32, GPS and/or GNSS may be used to determine a location
of each sensor.
Location determination represents one of the most energy-intensive processes
in IoT sensor
devices in an urban context. Due to off-nadir viewing angles with multi-height
buildings,
PVT may be cycled on and off repeatedly due to LOS. As such, the PVT may be
transitioned
off of the IoT sensor device and into a cloud-based or cloudlet ecosystem
which may save
immense battery energy. As battery life contributes to low Return on
Investment (ROT) with
frequent change throughout the sensor's lifetime, the systems and methods as
described
herein can remove the GPS computing power off of the chip-based sensor thus
improving
energy efficiency. The sensor's battery can then instead be used for
transmitting crop, soil,
and weather metrics for analysis by various AT algorithms.
100601 The sensor data may be used to generate crop and/or
weather metrics at 34.
Sensor data as described herein may be utilized by, for example, urban
farmers¨for
efficiencies in growing and resource utilization¨and for customers such as
cities¨for
measuring sustainability goals within smart city projects in regard to food
production and
climate resilience.
100611 Further, cumulative data effects can be amplified by
incorporating additional
rooftops/data or by offering data to adjacent customers using one or more
cloud or cloudlet
distributed data networks at 36. At 38, the application-based platform may be
used to
provide users a user interface or dashboard for site-specific and/or city-wide
metrics. Such
metrics may include, for example, city-wide UHI reductions, stormwater capture
information,
and microclimate data.
100621 Through the use of an information flow process 200 via a
smart city urban
agriculture data network as illustrated in Fig. 2, users may be enabled to
leverage cumulative
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data as described herein to shape future zoning regulations, future smart city
development
projects, and collaborations between private and public businesses.
100631 As illustrated in Fig. 3, a method 300 for developing a
rooftop index and
ranking system according to an embodiment of the present technology. The
rooftop index
and ranking system may index and rank agricultural spaces, such as rooftop
farms, based on
an order of magnitude for meeting and/or exceeding a prescribed list of
criteria. Such a
system may provide a key to developing a city roadmap for agriculture
implementation.
Ideal locations for agricultural spaces may be identified and ranked using a
method 300 as
described herein. The method 300, as described below, may implement one or
more machine
learning algorithms, and may be used by a user such as a city government using
a city-
specific GIS via digital software and/or applications.
100641 To develop a comprehensive algorithm capable of indexing
and ranking existing
rooftop infrastructure in a designated urban environment, a system such as an
artificial
intelligence model, machine learning training model, neural network, or other
system, may be
configured to filter out desirable attributes from undesirable attributes.
Training datasets
currently exist for different types of roof feature detection. A pitched roof
detection dataset
may be used to train a machine learning model to identify flat roofs and
pitched roofs in
image data. The pitch filter may, in some embodiments, be a custom algorithm
pipeline
configured to use LiDAR data to filter out any roofs that are not flat. First,
LiDAR elevation
data may be converted to slope using a geospatial data abstraction library The
geospatial
data abstraction library may be configured to read metadata and process raster
and vector
geographical data and may be used to develop a GIS roadmap. A given rooftop
may be
classified as flat if the number of pixels on the given rooftop that are less
than or equal to a
slope threshold make up more than an area threshold, which may be represented
as a percent
of an area of the rooftop. The slope threshold may be a hyperparameter
defining the slope
(e.g., in degrees) at which a pixel may be considered to be flat. The area
threshold may be a
hyperparameter defining the percentage of pixels on a rooftop which must be
flat in order for
a roof to be considered flat. The pitch filter algorithm may be configured to
convert all slope
values greater than the slope threshold to zero, and everything else to one.
If a pixel's slope
is determined to be greater than the slope threshold of x degrees, zero is
assigned, otherwise
one is assigned. If greater than x% of the pixels on the rooftop have a value
of 1, the roof
may be classified as flat, otherwise the roof may be classified as pitched. As
used herein, x
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degrees and x% are hyperparameters that are likely to change from city to
city. To determine
a flat area, the sum of the pixels and area are calculated. Zonal statistics
are then calculated,
before extracting flat areas from the statistics. Building and flat area
spatial polygons may
next be merged before the total flat area is calculated. Buildings whose flat
area is less than a
threshold square footage may be filtered from results. As used herein, a
machine learning
model may be an artificial intelligence model, a neural network, a
convolutional neural
network, or any type of system trained using training data to generate
outputs.
100651 At 302, a trained machine learning model or algorithmic
data pipeline (Flat Area
ID (FAID)) may be used to identify flat roofs. The FAID may in some
embodiments be one
or more algorithms and may employ a neural network. Based on LiDAR data, the
FAID may
be used to identify areas within a building footprint that are flat and
greater than a specified
minimum square footage. Output of FAID may be vector data such as a shapefile
with an
attribute that connects each polygon to a building footprint. To find
contiguous flat areas, a
raster of building height may be smoothed with a gaussian filter to remove any
pits, and a
raster of slope may be masked to one if slope is less than a particular angle,
such as 45
degrees, and zero if the slope is greater than a particular angle, such as 45
degrees. These
two new rasters may be multiplied and filtered to remove pixels that are
smaller than a
particular area, such as five square feet. The raster may then be converted to
polygons with a
region growing algorithm and intersected with a building footprint vector to
create a vector of
contiguous flat areas within building footprints. Finally, this new vector may
be filtered to
remove flat areas smaller than a specified square foot minimum
100661 In some embodiments, LiDAR data may be used to establish
hyperparameters
such as a slope or angle threshold and an area or size threshold. For example,
the model may
determine a roof is flat based on a determination that pixels on a given
rooftop are less than
or equal to a slope threshold.
100671 At 304, the flat roofs identified in 302 may be further
analyzed to determine
whether a roof meets a size or area threshold. Area threshold analysis may be
used to define
a percentage of pixels which must be flat in order to consider a roof as a
flat usable area.
Based on the determination that pixels are of a flat roof which meets or
exceeds an area
threshold, vector data may be derived.
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100681 The vector data derived from element 304 comprises a
baseline for a series of
multi-decision criteria assessments at elements 306a-g. Each feature layer
represents a
separate algorithm which may be used to derive accurate and measurable
information used in
a complex selection process. Feature layers may, for example, include the
following: usable
area, slope, load capacity, load volume, building height, closeness to a
point, parapet, etc.
100691 For example, an algorithm or AT system may be configured
to calculate shadows
and/or wind in input image data at element 306a. are easily incorporated into
both MCDA
layers and GIS layers.
100701 At 306b, a parapet detection algorithm may be used to
calculate a change in
slope at a determined offset from a building perimeter based on the slope
threshold derived at
302. The parapet detection algorithm starts by making a gdf representing a
boundary of the
buildings in question as well as making a gdf representing a one-meter buffer
around the
inside perimeter of the building. The two gdfs may be joined to make a ring
polygon around
the edge of the building. The ring polygon may be used to locate the parapet.
Next, the
median slope of the outer edge of the building may be calculated. Median slope
values may
next be added to each polygon and the parapet slope may be calculated. The
parapet slopes
may then be added to the building gdf. Any remarkable change in slope
detection at the
parapet location can determine if a parapet exists. Because parapets may
provide substantial
shielding from increased winds at elevation and provide a safety feature for
potential rooftop
windblown hazards, the parapet detection algorithm may be of use for selecting
viable
rooftops.
100711 At 306c, a shapefile reflecting contiguous flat areas
meeting a minimum criteria
for establishing a rooftop farm may be derived based on the vector data
generated at 304. In
some embodiments, slope, building footprint, area, and/or other factors may be
used to derive
the shapefile. Usable area may be derived from each unique FAID. For a given
FAD
polygon, the area attribute may be extracted from a .shp geometry and
multiplied by 10.7639
to convert from mA2 to ft^2. The raster data may next be polygonized using a
region growing
algorithm. A filter may be used to identify flat areas from the slope
threshold, convert flat
areas into polygons, spatially join buildings and flat areas, and convert all
multi polygons into
individual polygons. Area may next be calculated and any polygons under a
minimum
square foot threshold may be removed.
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[0072] At 306d, a load capacity may be determined from the vector
data generated at
304 in addition to building footprint data. For example, a load capacity may
be determined
by calculating an inverse of the flat area by performing a spatial difference
between the flat
area and each building footprint. The height and area of each of the resulting
polygons may
be calculated and multiplied as an estimate of volume (provided an assumption
that all
objects on the rooftops are rectangular). Finally, the volume of all objects
on a given
building may be summed to generate a total estimate of volume on a rooftop.
Cross-
referencing the resulting volume against building data, e.g., construction
type and year built,
the resulting algorithm may be used to provide an estimate for a structural
load capacity of
the building rooftop. In some embodiments, the volumetric and load capacity
findings may
be corroborated via a licensed structural engineer to assess the exact weight
per square foot of
load capacity for each building, including calculations of dry and/or wet
loads of additional
soil medium, crops, mechanical equipment, etc.
[0073] Building height 306e a may be determined using a composite
of LiDAR data.
The LiDAR data may be combined with the flat usable area developed in 304 to
determine
slope 306f. The closeness of any given building identified previously through
element 304 to
any geolocated point may be determined, based on FAID and point data in vector
format.
For a given FAID and a vector of points, a minimum distance to a point from
the centroid of
the FAID may be determined. Closeness to a point (306g) may be used to provide
a city with
points of interest (POIs) or distance from a POI to aid in the decision-making
when choosing
building sites For example, if the index locates a series of buildings across
a municipality, a
GIS roadmap may include different layers of information. Some layers of
information may
indicate socio-economic indicators, average temperatures, fresh food
locations, green space,
etc. Closeness to a point may allow for any building in the index to be
located within the
above layers of information and for a distance to be calculated to better
inform decisions. For
example, priority may be given to a building site that is far from any green
space, has higher
temperatures than the city-wide average, and has few fresh food and/or grocery
options in its
vicinity.
[0074] Different weighting criteria may be used in MCDA to
determine the desired
point and distance from buildings of interest identified as viable development
candidates.
Such criteria may be different for each city or user, and the weighting system
may be capable
of being tuned as needed to display the most pressing issues for a
municipality (e.g., distance
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from fresh food/store, distance from nearest green space, etc.). This list is
by no means
exhaustive, and one or more of these criteria or others not listed may apply.
100751 Incorporating these different features and elements as
critical layers of the
MCDA as described herein may enable an index of rooftop gardens to be tuned
and for
highly accurate results to be delivered The results may form the basis for a
custom roadmap
to be provided to cities, private industries, communities, and farmers in the
forms of one or
more of an interactive, editable, and scalable GIS, using layers of inputs
that represent
building characteristics, socio-economic factors, microclimate data, viability
for measurable
environmental impact, etc.
100761 Fig. 4 illustrates a data processing system or agriculture
network data platform
400 for smart city indexing and monitoring and transforming rooftop data into
usable data for
a multitude of user profiles for use with the methods and systems consistent
with the present
embodiment. The data processing system 400 consists of a plurality of
computing devices:
an index analysis computer 402, a monitoring computer 404, a data platform
computer 406,
and client devices 408, 410. Each computing device may be capable of
connecting to a
network 412, via cloud or other means. Each of the index analysis computer
402, monitoring
computer 404, data platform computer 406, and client devices 408, 410 may be,
for example,
a personal computer, tablet, mobile device, server, or other types of
computing devices. The
network 412 may include several networks, including but not limited to local
area networks
or wide area networks, hard-wired, wireless, etc The network 412 may be
considered the
Internet for illustrative purposes. Each of the index analysis computer 402,
monitoring
computer 404, data platform computer 406, and client devices 408, 410 may be
connected to
the network via an appropriate communication link.
100771 The index analysis computer 402, monitoring computer 404,
and data platform
computer 406 may operate as technology segment analysis tools and databases
for a smart
city data platform. While Fig. 4 illustrates the index analysis computer 402,
monitoring
computer 404, and data platform computer 406 as being separate computers, it
should be
appreciated each of the index analysis computer 402, monitoring computer 404,
and data
platform computer 406 may reside on a single machine or a multitude of
machines. Each of
the index analysis computer 402, monitoring computer 404, and data platform
computer 406
may comprise a machine learning analysis tool used to derive information
specific to the
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segment, whether it is an index and ratings algorithm, IoT crop and weather
data sync and
historical analysis tools and algorithms, or platform database tools which
search for patterns
across the full data network, collating collected data, and processing real
time decision
making reports and graphical analysis.
100781 For example, an index analysis computer 402 may comprise
one or more
machine learning analysis tools 402a and an index database 402b. The index
database may
host information relating to one or more city specific indices, roadmaps, GIS,
or other
ranking classification systems. Information hosted by the index database may
relate to the
identification of building assets as outlined in Fig. 2 and 3. The index
machine learning tools
may be warehoused or employed to derive patterns across the entire database.
Such patterns
may include, for example, changes in availability of rooftops for development
within a given
city, changes to the identification and ranking algorithm employed in
identifying and ranking
the rooftops, changes in infrastructure viability across multiple cities, and
patterns of
development across multiple cities. The analysis may result in graphic
information, report
generation, or periodic GIS map changes. The monitoring computer 404 may
comprise one
or more machine learning analysis tools 404a and a monitor database 404b.
Similar to the
index database 402band analysis tools 402a, the monitor database 404b may
warehouse all or
some of the data collected via IoT farm based sensors and geospatial
intelligence across a
city's network of farms, as well as from some or all cities in a portfolio.
The monitor
machine learning analysis tools 404a may host and/or analyze aggregated farm
data (sensor
and geospatial) for one or more farms across one or more cities, and/or host
and/or analyze
aggregate data across multiple cities for developing statistical and
quantitative benchmarking
by region, state, or country. The data platform computer 406 may comprise one
or more
machine learning (ML) analysis tools 406a and a platform database 406b. The
platform
database 406b may represents the PaaS that delivers city-wide visibility into
the agriculture
network ecosystem and may connect with other smart city initiatives or
programs. The UT
may include GIS, roadmaps, the index, or additional inputs from outside data
as well as
integrated, analyzed, and forecasted crop information and metrics and
microclimate data.
Projections or estimated yields across a city-wide network may be warehoused
for querying.
The ML analysis tools 406a may include graphics, reporting, statistical
analysis, or other
methods to determine food security, climate mitigation efforts and any
associated patterns
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across local or regional geographic areas, and/or to derive quantitative
measurement of the
combined cities metrics in a state, country, and/or global level.
100791 Each of the index database 402b, monitor database 404b,
and platform database
406b may comprise sufficient data architecture which stores or warehouses the
transformed
data for future querying, depending on the current or future allocation of
data needs
100801 In a certain embodiment of the present technology, the
data platform computer
406 may be configured to generate a user interface including one or more tabs
to allow a user
to access summarized, compared, or predicted crop data, weather metrics, crop
yields, and/or
other data points, such as on an hourly or daily basis or in real-time, on one
or more farms in
the network. The summary may be available through a GUI and/or statistical
displays. The
data platform computer 406 may be further configured to generate reports
containing such
information. The reports may be transmitted to one or computer systems such as
client
devices 408, 410.
100811 The data platform computer 406 may also allow a user to
leverage the data and
initial GIS to visualize and plan for the areas of implementation, building
sites, and schedule
of milestones. As the city scales, the platform, and the GIS scale with it,
adding to existing
layers as well as developing new layers that impact the platform's reach.
100821 As illustrated in Fig. 5, in some embodiments a method 500
of determining sites
or spaces viable for agriculture may be implemented using one or more of the
systems as
described herein. The method 500 may be implemented to determine viability of
rooftops
within an urban environment for growing crops, although it should be
appreciated the same
or similar methods may be implemented for other environments and for spaces
other than
rooftops, such as yards, parking lots, parks, etc. The method 500 enables
geospatial optical
resolutions, multispectral resolutions, and SAR data to be harmonized with
NDVI algorithms
to create new urban agriculture and building viability classification layers
for GIS.
100831 The method 500 may start 503 with a system 100 such as
illustrated in Fig. 1
and described above. One or more satellites 105 may be deployed and may be
configured to
capture image data as described above. The satellites 105 may acquire
multispectral data. It
should be appreciated that in some embodiments devices other than satellites
105 may
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acquire the data, such as planes or drones, or the data may be acquired from
one or more
databases such as third-party image resources available on the Internet.
100841 At 506, image data may be received by a computer system
such as a computing
device 135 or a user device such as a smartphone or tablet 130. The image data
may be, for
example, geospatial data received from a satellite
100851 In some embodiments, additional data may be received. For
example, along
with the image data, the computer system may receive or acquire a dataset
associated with a
city or other type of geographical location.
100861 At 509, the received image data may be processed using,
for example, an
artificial intelligence (Al) or machine learning (ML) model. As described
above, a model
may be trained to detect pixels from within one or more input images
corresponding to
potential rooftops or other sites which may be viable for agriculture
production. For
example, groups of pixels may be recognized as being of a relatively flat
space.
100871 At 512, a computer system may be configured to determine,
based on the
processing of the image data using the AT or ML model, a set of pixels of the
image data
meets a size threshold and an angle threshold. Whether a size threshold has
been met may be
determined based on a determination of whether an input image contains a
particular number
of connected pixels representing a certain land-size, such as measured in
square feet or acres.
Whether an angle threshold has been met may be determined based on a
determination as to
whether the object, land, building, etc., represented by the connected pixels
meeting the size
threshold is relatively flat or of a sufficiently minimum slope as to be
viable for agriculture.
In some embodiments other factors, as discussed above in relation to Fig. 3,
may be
determined, such as estimating a load capacity associated with the set of
pixels, determining
whether the set of pixels is surrounded by a parapet, etc.
100881 Based on determining a connected set of pixels meets a
particular size threshold,
angle threshold, and/or other factors, the set of pixels may be recorded as a
viable agriculture
space at 515. Recording the set of pixels may in some embodiments comprise
updating an
index as described above.
100891 A method 500 as described herein may be used to quickly
and efficiently
identify and investigate viable spaces within an area for agricultural
purposes. Such a
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method 500 may be particularly useful to determine spaces such as rooftops
within an urban
area for rooftop farming. Identifying urban areas which are viable for
farming, gardening, or
otherwise growing crops may be useful to, for example, a city government
100901 At 518, the method 500 may end. In some embodiments, an
application
platform may be used to provide descriptions of areas recognized as viable to
one or more
users. An application platform may enable city government departments to hold
one or more
subscription seats which may allow for variable data layers within a user
interface of the
application platform to be activated or deactivated, depending upon the nature
of the use case
and predetermined criteria designating the data usage, e.g., building
departments, planning
departments, parks departments, smart cities, IoT, ICT, etc. It should be
appreciated the list
of described city departments is meant to be neither exclusionary nor
exhaustive regarding
possible departmental interest or usage of the collected and harmonized data.
Data generated
in a method 500 as described herein may be utilized and leveraged for current
or future
developments differently across one or more departments. A custom city GIS may
be used
throughout different departments and the application platform may provide for
the ability to
toggle between visible and application-specific inactive layers. As deemed
necessary, such
layers may be visible to desired parties across multiple departments, farms,
private business,
communities, schools, institutions, etc. In some embodiments, access to the
data may be
managed through a data platform by a developer, by a designated department
manager, or
other party assigned at a time of subscription to services.
100911 As illustrated in Fig. 6, in some embodiments a method 600
method 600 of
monitoring space used for agricultural purposes, such as one or more rooftop
farms or
gardens or other spaces within an urban environment, may be implemented using
one or more
of the systems as described herein.
100921 The method 600 may start 603 in which one or more sensors
125a, 125b
positioned in or near an agricultural site, such as a rooftop farm, may be
deployed. The
sensors 125a, 125b may be, for example, weather sensors such temperature,
humidity, wind,
etc., soil sensors, or other types of sensors. The sensors 125a, 125b may be
installed near an
agricultural site or in the soil. In some embodiments, a plurality of sensors
125a, 125b may
be associated with a single agricultural site such as one garden or may be
associated with a
plurality of agricultural sites such as a plurality of rooftop farms.
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100931 At 606, a computer system may receive data from the one or
more sensors. The
data may be received by the computer system in a raw form such as raw sensor
data or may
be received following processing such as processing by a processor device
associated with
each sensor. For example, a sensor may be a part of a sensor device containing
the sensor as
well as a computing device. Whether the sensor data is received in a raw form
or in a
processed form, the computer system receiving the data may perform further
processing of
the data.
100941 In some embodiments, the computer system receiving the
data may be
configured to determine a location of the one or more sensors from which the
data is
received. The data may be received from the one or more sensors via a field
gateway 120 in
communication with a network 115 as illustrated in Fig. 1. Each sensor may be
associated or
in communication with one or more transmitters which may be configured to send
sensed
data to a field gateway. Such transmitters may include, for example, cellular
(NB-IoT,
LPWAN, LoRa WAN or LTE-M) data or Wi-Fi network capabilities. Each transmitter
may
be configured to transmit collected data via a cellular data or one or more Wi-
Fi networks. In
some embodiments, the data may be received along with location data or may be
received
along with data identifying the sensor from which the data originated.
Determining a
location of the sensors from which the data is received may comprise
identifying the sensor
and performing a data lookup using an identity of the sensor to determine a
location of the
sensor based on a table, list, index, or other form of data.
100951 In some embodiments, in addition to receiving sensor data,
the computer system
may also obtain image data such as VHR (<1m), geospatial optical resolutions,
multispectral
or hyperspectral resolutions, and/or SAR data,
100961 Based on the received data, one or more metrics may be
generated by the
computer system at 609. The metrics may be associated with the agriculture
space with
which each sensor is associated. In some embodiments, the computer system may
also
generate one or more recommendations based on the data received from the one
or more
sensors. For example, Al analytics, NDVI algorithms, SFI algorithms, or other
system
adapted to process any crop and/or micro-climate data may be configured to
generate an
output distributable to one or more users such as farmers. The output
generated may include,
for example, one or more recommendations based on the analyzed data.
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100971 In some embodiments, a plurality of agricultural sites,
such as urban agriculture
rooftops, may be interconnected digitally such as to form an urban agriculture
network. An
urban agriculture data network may thus be driven by IoT sensors, and the
network can be
integrated into one or more smart city networks and a circular data network
can be formed, a
cloud based data analysis platform adapted to receive and analyze the data. In
this way, a
plurality of farms or other types of agricultural sites, each collecting and
distributing data to a
cloud or cloudlet infrastructure, may, for example, contribute to a pooled and
anonymized
data platform. Access to the pooled and anonymized data may be distributed
across public
and private entities such as subscribers in good standing to a data network.
100981 At 612, a user interface comprising the generated metrics
may be generated.
The user interface may comprise a summary of data associated with the sensors
from which
the data was received. The user interface may also comprise any
recommendations generated
by the computer system. Using an application platform such as a decentralized
application-
based, web or mobile, data platform, the generated recommendations and metrics
may be
disseminated to a number of users. After generating the user interface, the
method 600 may
end 615.The processes and techniques disclosed herein have been described
above as a series
of steps. However, one or more of the steps can be optional and may be
skipped.
Additionally, the steps can be performed in a different order and/or by other
entity/entities
than described above.
100991 As illustrated in Fig 7, in some embodiments a method 700
of determining a
minimum viable threshold of contiguous flat roof area for agriculture
development (FAID)
may be implemented using one or more of the systems as described herein.
Building footprint
shapefiles and slope raster are passed through an FAID algorithm to produce a
shapefile of
FAIDs.
101001 The method 700 may start at 703 in which several types of
building datasets are
merged to calculate usable area, including, but not limited to: vector
building datasets,
national rasterized building datasets, or commercially available building
footprint shapefiles.
101011 At 706, building footprint shapefiles may be merged with
slope raster data.
Slope raster data may be determined by using LiDAR-derived raster of elevation
as an input
and passed through a region growing algorithm where rasters are converted to
polygons. or
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the raster package function may be used to compute plot slope and aspect for
each pixel. A
mean value of slope is calculated across all pixels within an FAID.
101021 At 709, contiguous groups of flat pixels may be grouped
together and vectorized
using a region growing algorithm. Spatial join may then be used to merge these
regions with
the building footprints to create the FAIDs and calculate the usable area at
712 The method
700 may end at 715.
101031 As illustrated in Fig. 8, in some embodiments a method 800
of determining a
slope raster used in calculating FAID, may be implemented using one or more of
the systems
as described herein.
101041 The method 800 may start with 803 in which a computer
system may receive
LiDAR data from a source such as one or more of an internal database, a remote
database,
and a cloud-based database.
101051 LiDAR-derived raster of elevation data 806, representing
the study area, may be
processed through a geographic data abstraction library processing library.
For example, a
formula to calculate slope in degrees from elevation data, arctan(rise/run),
between adjacent
pixels may be employed. The slope algorithm 809, may use the above function to
create a
slope raster at 812 to be used in determining the FAID in the method 700 and
the usable area
in 306e as described above. The method 800 may end at 812.
101061 Benefits, other advantages, and solutions to problems have
been described
herein regarding specific embodiments. However, the benefits, advantages,
solutions to
problems, and any elements that may cause any benefit, advantage, or solution
to occur or
become more pronounced are not to be construed as critical, required, or
essential features or
elements of the disclosure.
101071 No claim element herein is to be construed under the
provisions of 35 U.S.C.
Section 112, sixth paragraph, unless the element is expressly recited using
the phrase "means
for."
101081 In the foregoing specification, the disclosure has been
described with reference
to specific exemplary embodiments thereof. It will be evident that various
modifications may
be made thereto without departing from the broader spirit and scope as set
forth in the
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following claims. The specification and drawings are, accordingly, to be
regarded in an
illustrative sense rather than a restrictive sense.
101091 Some embodiments may be used in conjunction with various
devices and
systems, for example, a Personal Computer (PC), a desktop computer, a mobile
computer, a
laptop computer, a notebook computer, a tablet computer, a server computer, a
handheld
computer, a handheld device, a Personal Digital Assistant (PDA) device, a
handheld PDA
device, an on-board device, an off-board device, a mobile or portable device,
a consumer
device, a non-mobile or non-portable device, a wireless communication station,
a wireless
communication device, a wireless Access Point (AP), a wired or wireless
router, a wired or
wireless modem, a video device, an audio device, an audio-video (A/V) device,
a wired or
wireless network, a wireless area network, a Wireless Video Area Network
(WVAN), a Local
Area Network (LAN), a Wireless LAN (WLAN), a Personal Area Network (PAN), a
Wireless PAN (WPAN), and the like.
101101 Some embodiments may be used in conjunction with devices
and/or networks
operating in accordance with existing Wireless-Gigabit-Alliance (WGA)
specifications
(Wireless Gigabit Alliance, Inc. WiGig MAC and PHY Specification Version 1.1,
April
2011, Final specification) and/or future versions and/or derivatives thereof,
devices and/or
networks operating in accordance with existing IEEE 802.11 standards (IEEE
802.11-2012,
IEEE Standard for Information technology--Telecommunications and information
exchange
between systems Local and metropolitan area networks--Specific requirements
Part 11.
Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications,
March 29, 2012; IEEE802.11ac-2013 ("IEEE P802.1 lac-2013, IEEE Standard for
Information Technology - Telecommunications and Information Exchange Between
Systems
- Local and Metropolitan Area Networks - Specific Requirements - Part 11:
Wireless LAN
Medium Access Control (MAC) and Physical Layer (PHY) Specifications -
Amendment 4.
Enhancements for Very High Throughput for Operation in Bands below 6GHz",
December,
2013); IEEE 802.11 ad ("IEEE P802.11 ad-2012, IEEE Standard for Information
Technology -
Telecommunications and Information Exchange Between Systems - Local and
Metropolitan
Area Networks - Specific Requirements - Part 11: Wireless LAN Medium Access
Control
(MAC) and Physical Layer (PHY) Specifications - Amendment 3: Enhancements for
Very
High Throughput in the 60 GHz Band", 28 December, 2012); IEEE-802.11REVmc
("IEEE
802.11-REVmcTM/D3.0, June 2014 draft standard for Information technology -
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Telecommunications and information exchange between systems Local and
metropolitan area
networks Specific requirements; Part 11: Wireless LAN Medium Access Control
(MAC) and
Physical Layer (PHY) Specification"); IEEE802.11-ay (P802.1lay Standard for
Information
Technology¨Telecommunications and Information Exchange Between Systems Local
and
Metropolitan Area Networks--Specific Requirements Part 11: Wireless LAN Medium
Access
Control (MAC) and Physical Layer (PHY) Specifications--Amendment: Enhanced
Throughput for Operation in License-Exempt Bands Above 45 GHz)), IEEE 802.11-
2016
and/or future versions and/or derivatives thereof, devices and/or networks
operating in
accordance with existing Wireless Fidelity (Wi-Fi) Alliance (WFA) Peer-to-Peer
(P2P)
specifications (Wi-Fi P2P technical specification, version 1.5, August 2014)
and/or future
versions and/or derivatives thereof, devices and/or networks operating in
accordance with
existing cellular specifications and/or protocols, e.g., 3rd Generation
Partnership Project
(3GPP), 3GPP Long Term Evolution (LTE) and/or future versions and/or
derivatives thereof,
units and/or devices which are part of the above networks, or operate using
any one or more
of the above protocols, and the like.
101111 Some embodiments may be used in conjunction with one way
and/or two-way
radio communication systems, cellular radio-telephone communication systems, a
mobile
phone, a cellular telephone, a wireless telephone, a Personal Communication
Systems (PCS)
device, a PDA device which incorporates a wireless communication device, a
mobile or
portable Global Positioning System (GPS) device, a device which incorporates a
GPS
receiver or transceiver or chip, a device which incorporates an RFID element
or chip, a
Multiple Input Multiple Output (MIMO) transceiver or device, a Single Input
Multiple
Output (SIMO) transceiver or device, a Multiple Input Single Output (MISO)
transceiver or
device, a device having one or more internal antennas and/or external
antennas, Digital Video
Broadcast (DVB) devices or systems, multi-standard radio devices or systems, a
wired or
wireless handheld device, e.g., a Smartphone, a Wireless Application Protocol
(WAP) device,
a drone, a communications enabled drone or UAV, or the like.
101121 Some embodiments may be used in conjunction with one or
more types of
wireless communication signals and/or systems, for example, Radio Frequency
(RF), Infra-
Red (IR), Frequency-Division Multiplexing (FDM), Orthogonal FDM (OFDM),
Orthogonal
Frequency-Division Multiple Access (OFDMA), FDM Time-Division Multiplexing
(TDM),
Time-Division Multiple Access (TDMA), Multi-User MIIVIO (MU-MIIVIO), Spatial
Division
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Multiple Access (SDMA), Extended TDMA (E-TDMA), General Packet Radio Service
(GPRS), extended GPRS, Code-Division Multiple Access (CDMA), Wideband CDMA
(WCDMA), CDMA 2000, single-carrier CDMA, multi-carrier CDMA, Multi-Carrier
Modulation (MDM), Discrete Multi-Tone (DMT), Bluetooth , Global Positioning
System
(GPS), Wi-Fi, Wi-Max, ZigBeeTm, Ultra-Wideband (UWB), Global System for Mobile
communication (GSM), 2G, 2.5G, 3G, 3.5G, 4G, Fifth Generation (5G), or Sixth
Generation
(6G) mobile networks, 3GPP, Long Term Evolution (LTE), LTE advanced, Enhanced
Data
rates for GSM Evolution (EDGE), or the like. Other embodiments may be used in
various
other devices, systems and/or networks.
101131 Some demonstrative embodiments may be used in conjunction
with a WLAN
(Wireless Local Area Network), e.g., a Wi-Fi network. Other embodiments may be
used in
conjunction with any other suitable wireless communication network, for
example, a wireless
area network, a "piconet," a WPAN, a WVAN, and the like.
101141 Some demonstrative embodiments may be used in conjunction
with a wireless
communication network communicating over a frequency band of 5GHz and/or 60
GHz.
However, other embodiments may be implemented utilizing any other suitable
wireless
communication frequency bands, for example, an Extremely High Frequency (EHF)
band
(the millimeter wave (mmWave) frequency band), e.g., a frequency band within
the
frequency band of between 20GhH and 300GHz, a WLAN frequency band, a WPAN
frequency band, a frequency band according to the WGA specification, and the
like
101151 While the above provides just some simple examples of the
various device
configurations, it is to be appreciated that numerous variations and
permutations are possible.
101161 In the detailed description, numerous specific details are
set forth in order to
provide a thorough understanding of the disclosed techniques. However, it will
be
understood by those skilled in the art that the present techniques may be
practiced without
these specific details. In other instances, well-known methods, procedures,
components, and
circuits have not been described in detail so as not to obscure the present
disclosure.
101171 Although embodiments are not limited in this regard,
discussions utilizing terms
such as, for example, "processing,- "computing,- "calculating,- "determining,"
"establishing," "analyzing," "checking," or the like, may refer to
operation(s) and/or
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process(es) of a computer, a computing platform, a computing system, a
communication
system or subsystem, or other electronic computing device, that manipulate
and/or transform
data represented as physical (e.g., electronic) quantities within the
computer's registers and/or
memories into other data similarly represented as physical quantities within
the computer's
registers and/or memories or other information storage medium that may store
instructions to
perform operations and/or processes.
101181 Although embodiments are not limited in this regard, the
terms "plurality" and
"a plurality" as used herein may include, for example, "multiple" or "two or
more." The
terms "plurality" or "a plurality" may be used throughout the specification to
describe two or
more components, devices, elements, units, parameters, circuits, or the like.
For example, "a
plurality of stations- may include two or more stations.
101191 It may be advantageous to set forth definitions of certain
words and phrases used
throughout this document: the terms "include" and "comprise,- as well as
derivatives thereof,
mean inclusion without limitation; the term "or," is inclusive, meaning
and/or; the phrases
"associated with" and "associated therewith," as well as derivatives thereof,
may mean to
include, be included within, interconnect with, interconnected with, contain,
be contained
within, connect to or with, couple to or with, be communicable with, cooperate
with,
interleave, juxtapose, be proximate to, be bound to or with, have, have a
property of, or the
like; and the term "controller" means any device, system or part thereof that
controls at least
one operation, such a device may be implemented in hardware, circuitry,
firmware or
software, or some combination of at least two of the same. It should be noted
that the
functionality associated with any particular controller may be centralized or
distributed,
whether locally or remotely. Definitions for certain words and phrases are
provided
throughout this document and those of ordinary skill in the art should
understand that in
many, if not most instances, such definitions apply to prior, as well as
future uses of such
defined words and phrases.
101201 For purposes of explanation, numerous details are set
forth in order to provide a
thorough understanding of the present techniques. It should be appreciated
however that the
present disclosure may be practiced in a variety of ways beyond the specific
details set forth
herein. Furthermore, while the exemplary embodiments illustrated herein show
various
components of the system collocated, it is to be appreciated that the various
components of
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the system can be located at distant portions of a distributed network, or
within a dedicated
secured, unsecured, and/or encrypted system and/or within a network operation
or
management device that is located inside or outside the network.
101211 Thus, it should be appreciated that the components of the
system can be
combined into one or more devices or split between devices As will be
appreciated from the
following description, and for reasons of computational efficiency, the
components of the
system can be arranged at any location within the environment without
affecting the
operation thereof.
101221 Furthermore, it should be appreciated that the various
links, including the
communications channel(s) connecting the elements, can be wired or wireless
links or any
combination thereof, or any other known or later developed element(s) capable
of supplying
and/or communicating data to and from the connected elements. The term module
as used
herein can refer to any known or later developed hardware, circuitry,
software, firmware, or
combination thereof, that is capable of performing the functionality
associated with that
element. The terms determine, calculate, and compute and variations thereof,
as used herein
are used interchangeably and include any type of methodology, process,
technique,
mathematical operational or protocol.
101231 The systems and methods disclosed herein can also be
implemented as
instructions on a computer-readable information storage media that when
executed by one or
more processors cause to be performed any of the above aspects disclosed
herein.
101241 Embodiments of the present disclosure include a method of
determining
agriculture space viability, the method comprising: receiving image data;
processing the
image data with an artificial intelligence model; determining, based on the
processing of the
image data, a set of pixels of the image data meets one or more thresholds
associated with
agricultural viability; and based on determining the set of pixels meets the
one or more
thresholds, recording the set of pixels as a viable agriculture space.
101251 Aspects of the above method include wherein the one or
more thresholds
comprise one or more of a size threshold and an angle threshold.
101261 Aspects of the above method include the method further
comprising identifying
the set of pixels as a rooftop.
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[0127] Aspects of the above method include wherein the
agriculture space is a rooftop.
[0128] Aspects of the above method include wherein the image data
is geospatial data
received from a satellite.
[0129] Aspects of the above method include the method further
comprising, prior to
recording the set of pixels as the viable agriculture space, estimating a load
capacity
associated with the set of pixels.
[0130] Aspects of the above method include the method further
comprising, prior to
recording the set of pixels as the viable agriculture space, identifying a
parapet associated
with the set of pixels.
[0131] Aspects of the above method include wherein recording the
set of pixels
comprises updating an index.
[0132] Aspects of the above method include the method further
comprising, prior to
processing the image data, receiving a dataset associated with a city.
[0133] Embodiments include a user device comprising. a processor;
and a computer-
readable storage medium storing computer-readable instructions which, when
executed by
the processor, cause the processor to execute a method, the method comprising:
receiving
image data; processing the image data with an artificial intelligence model;
determining,
based on the processing of the image data, a set of pixels of the image data
meets one or more
thresholds associated with agricultural viability; and based on determining
the set of pixels
meets the one or more thresholds, recording the set of pixels as a viable
agriculture space.
[0134] Aspects of the above user device include wherein the one
or more thresholds
comprise one or more of a size threshold and an angle threshold.
[0135] Aspects of the above user device include the method
further comprising
identifying the set of pixels as a rooftop.
[0136] Aspects of the above user device include wherein the
agriculture space is a
rooftop.
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[0137] Aspects of the above user device include wherein the image
data is geospatial
data received from a satellite.
[0138] Aspects of the above user device include the method
further comprising, prior to
recording the set of pixels as the viable agriculture space, estimating a load
capacity
associated with the set of pixels
[0139] Aspects of the above user device include the method
further comprising, prior to
recording the set of pixels as the viable agriculture space, identifying a
parapet associated
with the set of pixels.
[0140] Aspects of the above user device include wherein recording
the set of pixels
comprises updating an index.
[0141] Embodiments include a computer program product comprising.
a non-transitory
computer-readable storage medium having computer-readable program code
embodied
therewith, the computer-readable program code configured, when executed by a
processor, to
execute a method, the method comprising: receiving image data; processing the
image data
with an artificial intelligence model; determining, based on the processing of
the image data,
a set of pixels of the image data meets one or more thresholds associated with
agricultural
viability; and based on determining the set of pixels meets the one or more
thresholds,
recording the set of pixels as a viable agriculture space.
[0142] Aspects of the above computer program product include
wherein the one or
more thresholds comprise one or more of a size threshold and an angle
threshold.
[0143] Aspects of the above computer program product include the
method further
comprising identifying the set of pixels as a rooftop.
[0144] Aspects of the above computer program product include
wherein the agriculture
space is a rooftop.
[0145] Aspects of the above computer program product include
wherein the image data
is geospatial data received from a satellite.
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101461 Aspects of the above computer program product include the
method further
comprising, prior to recording the set of pixels as the viable agriculture
space, estimating a
load capacity associated with the set of pixels.
101471 Aspects of the above computer program product include the
method further
comprising, prior to recording the set of pixels as the viable agriculture
space, identifying a
parapet associated with the set of pixels.
101481 Aspects of the above computer program product include
wherein recording the
set of pixels comprises updating an index.
101491 Embodiments include a method of monitoring agriculture
space, the method
comprising: receiving data from one or more sensors, wherein each of the one
or more
sensors is associated with an agriculture space, generating one or more
metrics indicative of
farming viability associated with the agriculture space based on the data
received form the
one or more sensors; and generating a user interface comprising the generated
metrics.
101501 Aspects of the above method include wherein the one or
more metrics comprise
one or more of a temperature, a precipitation level, wind event data, air
pressure change data,
fertilization indicator, and a UHI temperature index.
101511 Aspects of the above method include the method further
comprising generating
one or more recommendations based on the data received from the one or more
sensors,
wherein the one or more recommendations comprise one or more of a water
recommendation,
fertilizer recommendation, crop recommendation, planting recommendation,
harvesting
recommendation, and soil augmentation recommendation. Aspects of the above
method
include wherein the data comprises data processed by a processor of a device
comprising the
sensor.
101521 Aspects of the above method include the method further
comprising processing
the data received from the one or more sensors
101531 Aspects of the above method include the method further
comprising determining
a location of the one or more sensors.
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101541 Aspects of the above method include wherein the one or
more sensors comprise
one or more of a soil sensor and a weather sensor.
101551 Aspects of the above method include wherein the data is
received from the one
or more sensors via a field gateway.
101561 Aspects of the above method include wherein the user
interface comprises a
summary of data associated with the sensors.
101571 Aspects of the above method include the method further
comprising generating
one or more recommendations based on the data received from the one or more
sensors,
wherein the user interface further comprises the recommendations.
101581 Embodiments include a user device comprising. a processor,
and a computer-
readable storage medium storing computer-readable instructions which, when
executed by
the processor, cause the processor to execute a method, the method comprising:
receiving
data from one or more sensors, wherein each of the one or more sensors is
associated with an
agriculture space; generating one or more metrics indicative of farming
viability associated
with the agriculture space based on the data received form the one or more
sensors; and
generating a user interface comprising the generated metrics.
101591 Aspects of the above user device include wherein the one
or more metrics
comprise one or more of a temperature, a precipitation level, wind event data,
air pressure
change data, fertilization indicator, and a UHI temperature index.
101601 Aspects of the above user device include the method
further comprising
generating one or more recommendations based on the data received from the one
or more
sensors, wherein the one or more recommendations comprise one or more of a
water
recommendation, fertilizer recommendation, crop recommendation, planting
recommendation, harvesting recommendation, and soil augmentation
recommendation.
101611 Aspects of the above user device include wherein the data
comprises data
processed by a processor of a device comprising the sensor.
101621 Aspects of the above user device include the method
further comprising
processing the data received from the one or more sensors.
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[0163] Aspects of the above user device include the method
further comprising
determining a location of the one or more sensors.
[0164] Aspects of the above user device include wherein the one
or more sensors
comprise one or more of a soil sensor and a weather sensor.
[0165] Aspects of the above user device include wherein the data
is received from the
one or more sensors via a field gateway.
[0166] Aspects of the above user device include wherein the user
interface comprises a
summary of data associated with the sensors.
[0167] Aspects of the above method include the method further
comprising generating
one or more recommendations based on the data received from the one or more
sensors,
wherein the user interface further comprises the recommendations.
[0168] Embodiments include a computer program product comprising;
a non-transitory
computer-readable storage medium having computer-readable program code
embodied
therewith, the computer-readable program code configured, when executed by a
processor, to
execute a method, the method comprising: receiving data from one or more
sensors, wherein
each of the one or more sensors is associated with an agriculture space;
generating one or
more metrics indicative of farming viability associated with the agriculture
space based on
the data received form the one or more sensors; and generating a user
interface comprising
the generated metrics.
[0169] Aspects of the above computer program product include
wherein the one or
more metrics comprise one or more of a temperature, a precipitation level,
wind event data,
air pressure change data, fertilization indicator, and a UT-II temperature
index.
[0170] Aspects of the above computer program product include the
method further
comprising generating one or more recommendations based on the data received
from the one
or more sensors, wherein the one or more recommendations comprise one or more
of a water
recommendation, fertilizer recommendation, crop recommendation, planting
recommendation, harvesting recommendation, and soil augmentation
recommendation.
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101711 Aspects of the above computer program product include
wherein the data
comprises data processed by a processor of a device comprising the sensor.
101721 Aspects of the above computer program product include the
method further
comprising processing the data received from the one or more sensors.
101731 Aspects of the above computer program product include the
method further
comprising determining a location of the one or more sensors.
101741 Aspects of the above computer program product include
wherein the one or
more sensors comprise one or more of a soil sensor and a weather sensor.
101751 Aspects of the above computer program product include
wherein the data is
received from the one or more sensors via a field gateway.
101761 Aspects of the above computer program product include
wherein the user
interface comprises a summary of data associated with the sensors.
101771 Aspects of the above computer program product include the
method further
comprising generating one or more recommendations based on the data received
from the one
or more sensors, wherein the user interface further comprises the
recommendations.
101781 Aspects thus also include: a system on a chip (SoC)
including any one or more
of the above aspects disclosed herein; one or more means for performing any
one or more of
the above aspects disclosed herein; and/or any one or more of the aspects as
substantially
described herein.
101791 For purposes of explanation, numerous details are set
forth in order to provide a
thorough understanding of the present embodiments. It should be appreciated
however that
the techniques herein may be practiced in a variety of ways beyond the
specific details set
forth herein.
101801 Furthermore, while the exemplary embodiments illustrated
herein show the
various components of the system collocated, it is to be appreciated that the
various
components of the system can be located at distant portions of a distributed
network, such as
a communications network and/or the Internet, or within a dedicated secure,
unsecured and/or
encrypted system Thus, it should be appreciated that the components of the
system can be
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combined into one or more devices or collocated on a particular
node/element(s) of a
distributed network, such as a data processing or image processing network. As
will be
appreciated from the following description, and for reasons of computational
efficiency, the
components of the system can be arranged at any location within a distributed
network
without affecting the operation of the system.
101811 While the above-described flowcharts have been discussed
in relation to a
particular sequence of events, it should be appreciated that changes to this
sequence can
occur without materially affecting the operation of the embodiment(s).
Additionally, the
exact sequence of events need not occur as set forth in the exemplary
embodiments.
Additionally, the exemplary techniques illustrated herein are not limited to
the specifically
illustrated embodiments but can also be utilized with the other exemplary
embodiments and
each described feature is individually and separately claimable.
101821 Additionally, the systems, methods and protocols can be
implemented to
improve one or more of a special purpose computer, a programmed microprocessor
or
microcontroller and peripheral integrated circuit element(s), an ASIC or other
integrated
circuit, a digital signal processor, a hard-wired electronic or logic circuit
such as discrete
element circuit, a programmable logic device such as PLD, PLA, FPGA, PAL, an
image
processing or big data processing device, any comparable means, or the like In
general, any
device capable of implementing a state machine that is in turn capable of
implementing the
methodology illustrated herein can benefit from the various communication
methods,
protocols, and techniques according to the disclosure provided herein
101831 Examples of the processors as described herein may
include, but are not limited
to, at least one of Qualcomm Snapdragon 800 and 801, Qualcomm Snapdragon
610
and 615 with 4G LTE Integration and 64-bit computing, Apple A7 processor with
64-bit
architecture, Apple M7 motion coprocessors, Samsung Exynos series, the
Intel
CoreTM family of processors, the Intel Xeon family of processors, the Intel
AtomTM
family of processors, the Intel Itanium family of processors, Intel Core i5-
4670K and
i7-4770K 22nm Haswell, Intel Core i5-3570K 22nm Ivy Bridge, the AMD FXTM
family
of processors, AMD FX-4300, FX-6300, and FX-8350 32nm Vishera, AMID Kaveri
processors, Texas Instruments Jacinto C6000TM automotive infotainment
processors, Texas
Instruments OMAPTm automotive-grade mobile processors, ARM CortexTMM
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processors, ARM Cortex-A and ARM926EJ-STm processors, Broadcom(1D AirForce
BCM4704/BCM4703 wireless networking processors, the AR7100 Wireless Network
Processing Unit, other industry-equivalent processors, and may perform
computational
functions using any known or future-developed standard, instruction set,
libraries, and/or
architecture.
[0184] Furthermore, the disclosed methods may be readily
implemented in software
using object or object-oriented software development environments that provide
portable
source code that can be used on a variety of computer or workstation
platforms.
Alternatively, the disclosed system may be implemented partially or fully in
hardware using
standard logic circuits or VLSI design. Whether software or hardware is used
to implement
the systems in accordance with the embodiments is dependent on the speed
and/or efficiency
requirements of the system, the particular function, and the particular
software or hardware
systems or microprocessor or microcomputer systems being utilized. The
communication
systems, methods and protocols illustrated herein can be readily implemented
in hardware
and/or software using any known or later developed systems or structures,
devices and/or
software by those of ordinary skill in the applicable art from the functional
description
provided herein and with a general basic knowledge of the computer and
telecommunications
arts.
[0185] Moreover, the disclosed methods may be readily implemented
in software
and/or firmware that can be stored on a storage medium to improve the
performance of a
programmed general-purpose computer with the cooperation of a controller and
memory, a
special purpose computer, a microprocessor, or the like In these instances,
the systems and
methods can be implemented as a program embedded on a personal computer such
as an
applet, JAVA® or CGI script, as a resource residing on a server or
computer
workstation, as a routine embedded in a dedicated communication system or
system
component, or the like. The system can also be implemented by physically
incorporating the
system and/or method into a software and/or hardware system, such as the
hardware and
software systems of a server.
[0186] It is therefore apparent that there has at least been
provided systems and
methods for improved agricultural optimization and data processing. While the
embodiments
have been described in conjunction with a number of embodiments, it is evident
that many
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alternatives, modifications, and variations would be or are apparent to those
of ordinary skill
in the applicable arts. Accordingly, this disclosure is intended to embrace
all such
alternatives, modifications, equivalents, and variations that are within the
spirit and scope of
this disclosure.
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Representative Drawing